Enterprise AI powered by your company’s knowledge.
Company context and governance that put trusted AI to work across your enterprise.
Connect fragmented data into permission-aware company context. Search it, ground AI applications in it, and put it to work in Enterop and your connected tools—with access and policy controls throughout.
Step 1 of 4
Planning & orchestration
Plans the steps, tools and approvals for each request.
Two lookups: the approved total, then the contingency
Step 2 of 4
Knowledge & relationships
Links people, projects and decisions to their sources.
Atlas → budget v3 → the finance committee’s approval
Step 3 of 4
Context engine & memory
Assembles current, task-ready context and memory.
Budget v3 (12 September) replaces the v2 draft
Step 4 of 4
Permissions & policy
Checks access and policy on every read and action.
Rafael can open both sources
The foundation is already running.
Tested in our Redwood demo using a public enterprise dataset.
0
permission leaks observed
Across 4,000 tested searches. 94,440 restricted documents never surfaced.
1 minute
for removed access to reach answers
38 to 66 seconds in our measured Slack and Google Drive tests.
99.8%
of answer sentences supported by their cited source
461 of 462 in one benchmark run.
512,000
documents indexed across several tools
Loaded into our Redwood demo with zero failed jobs.
86%
of questions retrieve the right source
On the public benchmark’s questions, in one measured run.
0.13¢
average model inference cost per answer
Across 720 answers in one run. Excludes retrieval, indexing, storage, hosting, engineering and support.
See the applications at work.
The context, controls and applications for enterprise AI.
Pick what you want to run. The map shows which parts of the platform it uses.
Models & AI control
Choose models and control how AI is used.
Company Brain
Ground AI in company context: enterprise, personal and trust, connected.
Governance & protection
Enforce access and policy wherever AI runs.
Admin & operations
Applications draw on the whole Company Brain, with permission-aware retrieval on every answer.
Foundation
Company knowledge AI can work with.
Shared platform
Your models. Your policies. Your way of working.
Applications
Find answers. Create. Collaborate. Move work forward.
AI adoption brings new demands on context, cost, and control.
As AI expands across teams and tools, enterprises need to power it with current company knowledge, ensure efficient model use, maintain consistent safeguards, and have a reliable way to evaluate results.
Understand the business
Knowledge is fragmented.
Enterop: Connect documents, conversations and records across systems.
AI lacks shared company context.
Enterop: Link people, projects, decisions and relevant working context.
Context becomes stale.
Enterop: Update connected knowledge as records and access change.
Answers are difficult to verify.
Enterop: Show sources and check generated claims against evidence.
Token use and latency are hard to control.
Enterop: Prepare focused context and use task-aware planning and controls.
Model choice is an ongoing decision.
Enterop: Support model choice and routing for different tasks.
Context is difficult to reuse.
Enterop: Extend governed context into supported applications and agents.
Security goes beyond access permissions.
Enterop: Combine sensitive-content policies with runtime protections.
Governance is fragmented.
Enterop: Apply identity, permissions, policies, approvals and audit across AI use.
Deployment and data requirements constrain adoption.
Enterop: Offer supported hosting, residency and retention controls.
Useful answers still need manual follow-through.
Enterop: Support creation, collaboration and governed workflows.
Work is reactive and loses continuity.
Enterop: Use memory, shared context and timely updates.
Leaders need evidence for where to apply AI.
Enterop: Understand workflows and design worthwhile improvements.
AI quality and value are difficult to prove.
Enterop: Evaluate performance and business value against agreed measures.
Google Drive
Atlas budget v2
Slack
“v3 is approved, I think?”
Jira
Last updated 8 months ago
Fwd: budget (final) (2)
Four tools, four versions of the truth, and no link between them.
Assistant
Context rebuilt
Agent
Context rebuilt
Plug-in
Context rebuilt
Tokens per requestWhich model?
Every tool rebuilds the same context, and pays for it again.
Company document
Open to Finance only
Unapproved AI tool
Pasted in. Access rules gone.
Chat toolIts own permissions
Agent builderNo approvals, no audit
Plug-inData leaves your region
Each tool sets its own rules, and copying a file skips all of them.
An answer
“The 50% step needs an approver.”
Copy it into an email
Update the ticket by hand
Chase the approver on Thursday
Time saved? Nobody measured it.
Follow one request through Enterop.
Pick a task. See the sources Enterop considers, the context and controls it applies, and the work it enables.
Step 3 of 3 · Agents, applications and work
Writes an answer where every sentence cites a source Rafael can open.
Google DriveUsed · Finance
Atlas budget v3 (approved)
Google DriveOutdated
Atlas budget v2 (draft)
SlackUsed · Finance
#atlas-finance
JiraNot needed
ATL-214 canary rollout
Meeting notesNot needed
Atlas go/no-go
Planning & orchestrationChecked
Plans two lookups, the approved total and where the contingency is held, then a cited answer.
Knowledge & relationshipsChecked
Atlas → budget v3 → the finance committee’s approval on 12 September.
Context engine & memoryChecked
Budget v3 replaces the v2 draft, so the older figure is left out.
Permissions & policyChecked
Rafael can open both sources. Anika, in Engineering, could open neither and would get nothing from them.
Cited answer for RafaelReady
The approved FY27 budget for Project Atlas is $2.4M [1], including a $300K contingency held by Finance [2].
[1] Atlas budget v3, approved 12 September · [2] #atlas-finance
Three platform capabilities, shown on Redwood.
Prepare the right context for every AI task.
Plans searches, retrieves relevant evidence and prepares focused context for frontier models—designed to reduce latency and cost while improving response quality across applications.
“Why is the Atlas rollout stuck at 25%, and what needs sign-off?”
Context prepared for the Assistant · 5 permitted sources, focused passages
1 · Jira · ATL-214
Canary at 25% since Tuesday. Next step blocked on approval.
2 · Slack · #atlas-launch
“We can’t go to 50% until a rollout-approver signs off.”
3 · Confluence · Rollout policy
From 25% upward, each step needs rollout-approver approval.
4 · GitHub · runtime#1182
p95 latency fix merged on Wednesday.
5 · Meeting notes · Atlas go/no-go
Approver may be out until Thursday.
Only sources this person can open. Context differs by task and by person.
Search results
- ATL-214 canary rollout
Jira · at 25%, next step waiting for approval - #atlas-launch · approver thread
Slack · “We can’t go to 50% until…” - Rollout policy
Confluence · approval needed from 25% upward - runtime#1182
GitHub · p95 latency fix merged
Enterop Work Intelligence
Understand the work. Design a better way.
Find opportunities, design practical solutions and present clear recommendations—with the evidence, expected benefit and steps needed to act.
“Where could launches move faster?”
Opportunity 1 of 4
Book rollout approvers before the 25% step
Evidence you can open
- Approvals wait longest from 25% upward
- Repeated “who can approve?” threads in #atlas-launch
- Approver calendars aren’t checked before a soak starts
Proposed solution
- Check availability when the 5% soak starts · Calendar, Jira
- Request approval ahead of time Approval point
- Remind the approver when the soak completes
- Record the decision on the rollout ticket
Expected benefit and effort
Expected: about 7 hours faster per rollout
Assumes the wait from 25% drops from 9.6 to about 2.5 hours.
Effort: low · needs calendar and Jira access
Expected benefit, not a measured result.
Enterop AI Outcomes · in the Admin Console
Agree on success. Measure what AI delivers.
Define success together at the start. Track AI performance and business value against agreed baselines—with transparent evidence, assumptions and calculations.
Agreed at the start
- Measure
- Hours waiting for rollout approval
- Baseline
- 9.6 hours median, July–September
- Success
- Under 3 hours median
- Period
- October–December
- Sources
- Rollout logs, Jira
- Owner
- Head of release engineering
Observed in October
Measured4.1 hours median
Down from 9.6 hours, across 5 rollouts. Target not yet met.
Waiting time saved
MeasuredAbout 27 hours in October
5 rollouts × 5.5 hours less waiting
Launch days gained
EstimateAbout 4 days a quarter
Assumes 14 rollouts a quarter at October’s pace
Benefits against the agreed baseline. Compare them with your own spending; this is not a net ROI figure.
Built for how enterprises protect information.
Your own environment
Each customer runs on a dedicated Enterop environment, in the region you choose. Nothing is shared with other customers.
Access checked on every answer
Enterop checks each person’s access, synced from your tools, on every search and answer. A removed permission reaches answers in about a minute.
Not used to train models
Your data isn’t used to train AI models. Model calls go to providers under terms that don’t allow training on it.
Every claim cited
Answers cite the documents they come from, so anyone can check a claim against its source.
Estimate the time Enterop could give back.
Change any assumption and the estimate updates as you type. It’s a starting point for a conversation, not a promise.
A shared foundation for how your enterprise uses AI.
Enterop grew out of building our own carbon sequestration project development company. The tools we built for ourselves revealed a wider need: an AI-native platform that helps enterprises put the latest AI advances to work, grounded in company knowledge and governed by design.
We’re working with a few large enterprises and opening a small number of additional design partnerships to shape Enterop around real systems, priorities and workflows.
Our vision is for every employee, application and agent to work from shared company context, with consistent governance across the enterprise.
- Pushkar RimmalapudiCo-founder
- Akshith BathenaCo-founder
Bringing AI into your enterprise?
Talk with the founders about a design partnership shaped around your systems, priorities and workflows.
Talk to the foundersLet’s put AI to work across your enterprise—on your terms, with security built in.
Request a demo or explore a design partnership with the founders.
Questions we often hear.
How does a design partnership work?
We start with one team and the sources it relies on, run Enterop on a dedicated environment for you, and work closely with your team to shape it around real workflows.
Which tools does Enterop connect to?
We start with your document storage, such as Google Drive, and file uploads, then connect the other tools your team relies on as part of the partnership.
Is our data used to train AI models?
No. Your data stays in your own environment, and model calls go to providers under terms that don’t allow training on it.
Who can see what in Enterop?
Only what they can already open in the original tool. Enterop checks each person’s access on every search and answer, and a removed permission reaches answers in about a minute.
How is pricing set?
We agree pricing with each design partner, based on the teams and work in scope. Tell us what you’d like to explore and we’ll suggest a starting point.